Xiao Xiao

TL;DR: Using Monte Carlo simulations, it is demonstrated that the error distribution determines which method performs better, with NLR better characterizing data with additive, homoscedastic, normal error and LR better characterizes data with multiplicative, heteroscedastics, lognormal error.

TL;DR: A synthesis of methods based on rarefaction curves that allow more explicit analyses of spatial and sampling effects on biodiversity comparisons are described, using a case study of nutrient additions in experimental ponds to illustrate how this multi-dimensional and multi-scale perspective informs the responses of biodiversity to ecological drivers.

TL;DR: This work shows that a simple maximum entropy model produces a truncated log-series distribution that can predict between 83% and 93% of the observed variation in the rank abundance of species across 15 848 globally distributed communities including birds, mammals, plants, and butterflies.

TL;DR: This article decompose scale-specific changes in richness into proximate components attributed to: 1) the species abundance distribution, 2) density of individuals, and 3) the spatial arrangement of individuals.

TL;DR: Across all datasets combined the log-series, Poisson lognormal, and negative binomial all yield similar overall fits to the data, and when correcting for differences in the number of parameters thelog-series generally provides the best fit to data.